The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
A present day challenge is the ability to rapidly and accurately identify similar and repeating, but “ad hoc” financial transactions in complex systems based on large scale databases. Such databases may contain a large plurality of credit card transactions, often thousands, millions or more such transactions. In addition, such databases often contain textual content that is input by a large number of different agents, so differences in documentation approach, the vernacular used, and even the various terms used in describing the transaction or parties to the transactions are very common. Still further, related financial transaction information may be contained in more than one database of a given organization and the data may be incomplete. Thus, there exists a continual challenge to extract useful, actionable information from large volumes of current and historical free text data, which leads to a multitude of correlation issues that add to the complexity of the financial activity analysis. This may result in a large variety of computational and analytic challenges when attempting to analyze financial transaction data. The usual result is long analysis mitigation times which may lead to high costs when attempting to have individuals manually analyze large amounts of financial transaction data. Such activity may be expensive, burdensome and unacceptable for many businesses and governmental operations. Thus, the existence of these ad hoc business relationships may only rarely be discovered through manual analysis of financial transaction information because it simply takes too much time, or is too costly, to have individuals attempt to obtain such information from existing databases.
Further to the above, manual analysis by individuals may sometimes take months to accomplish and often require a team of experts, which may also introduce inconsistencies in the analysis results provided. Tools that the human analysts may use may be rules-based models, relational databases and query systems, and data mining systems.
Existing systems also may be limited in the ability to use whole text capture and are thus limited in their ability to relate entities in a complex and subtle manner. An entity may be defined as a data element that has its own set of attributes or descriptors, for example cities, cars, specific financial institutions, etc. Modern data mining solutions are typically reductive and may lose a substantial amount of valuable information during a searching process. These reductive solutions also tend to lose the subtleties of the data that often may be key to determining desirable patterns that do not repeat often. Modern data mining solutions may also be time consuming and costly in terms of manpower hours, as well as processor intensive.
With many present day financial transaction databases, much of the valuable association data between transactions can also be lost because the analyst may be “forced” into characterizing a transaction, during a computer assisted searching operation, by pre-defined characteristics. For example, many database systems have drop-down menus that allow the analyst to select only certain categories or certain words when performing searching activities. The predetermined categories may not contain enough detail to adequately address all the associations between transactions, therefore omitting relationships or details that can be of significant assistance in determining a desired result or that may assist in an analytical process. For example, a relational database might force a purchase to be described as “office furniture supplies” or “office software”. Free text might describe the transaction as “office furniture layout program.” Thus, in this example it would only be in the free text format that the true nature of the problem can be accurately described. Conventional rules based database systems can also be difficult to adapt or modify to accommodate to changing business conditions.
If an organization has repeating purchases from the same vendor it may be advantageous to establish a formal relationship with the vendor. This has the advantage that each party can negotiate terms to the best value of both parties. Furthermore, such a negotiated business arrangement may reduce costs to both parties, especially if the standard credit card transaction fee imposed on the seller can be reduced.
In the example of ad hoc financial transactions, sometimes purchases are made by an organization with a credit card by an individual employee of the organization. However, it may be more advantageous to the organization, either for cost reasons or other reasons, if the purchase was made with a purchase order. These instances can be difficult to find because the individual purchaser may not be motivated to formalize the relationship with the seller from which the purchase was made. Or the seller may prefer the ad hoc credit card transactions with various individuals of the organization because this allows the volume of sales to the organization to be more easily shielded, or so that terms of the sale can be unilaterally set by the vendor. This may make it more difficult for the organization to identify those sellers with which it would otherwise seek to negotiate volume discounts with. Re-occurring payments for parking, office supplies and software purchases may be undetected by the existing systems but may be actually form significant, re-occurring credit card purchases that are difficult or impossible to detect from a larger plurality of transactions.
The existing rules based systems may also be inherently limited by the irregular payment schedules across a timeline that is associated with payment on a transaction. Existing rules based systems may also be inherently limited by inadequate categorical association and free text characterization that differs across organizational boundaries and by the sheer number of transactions that must be analyzed to spot re-occurring transactions.
Existing relational database manipulation tools may be able find keywords, but the perspective is typically that of the relational database designer, not the individual that relates to the current situation or transaction. Relational databases often do not account for all the entities that are mention in a free text query by a user, and may also fail to understand those characteristics that make the transactions uniquely similar, or dissimilar.
As a further example to the challenges associated with analyzing ad hoc business relationships and the detection of such relationships using conventional relational databases, consider the limitations that may be imposed by some conventional systems involving split payments via a credit card. Split payments with a credit card may be prohibited by both business transaction process documentation. Split payments for computing equipment, office furniture and software purchases may go undetected by present day systems but readily recognized as significant, split credit card purchases when aggregated.
Some present day systems involve attempting to categorize all credit card transactions into a relatively large plurality, (e.g., 100-300) different bins using a rule-based approach. The bins are then individually analyzed looking for repeating transactions. The challenge with this approach is that there may be hundreds or more of such bins, with each bin containing thousands or more credit card transaction entries. And to complicate matters further, many times related transactions are placed in different bins because of the limitations of rules-based system that is being used to categorize or organize the transactions. Still another challenge with the above-described human centric approach is that humans often times bias searches by not being consistent across a time line when it comes to the terminology being used to query across large databases for given types of information.